Collaborative filtering systems represent services of personalized that aim at predicting a user’s interest on some items available in the application systems. With the development of electronic commerce, the number of users and items grows rapidly, resulted in the sparsity of the user-item rating dataset. Poor quality is one major challenge in collaborative filtering recommender systems. Sparsity of users’ ratings is the major reason causing the poor quality and the traditional similarity measure methods make poor in this situation. To address this issue, this paper proposes a collaborative filtering recommendation algorithm based on the item classification to pre-produce the ratings. This approach classifies the items to predict the ratings of the vacant values where necessary, and then uses the item-based collaborative filtering to produce the recommendations. The collaborative filtering recommendation method based on item classification prediction can alleviate the sparsity problem of the user-item rating dataset, and can provide better recommendation than traditional collaborative filtering.